Getting Loan Clients to Recommend Financial Service Providers: The Role of Satisfaction, Trust and Information Quality
Bibliographic record
Abstract
This paper examines the role of satisfaction, trust and information quality as determinants of loan clients’ recommendation of financial service providers in a developing country. Drawing on existing literature, a conceptual model was developed and validated with data from loan clients in Ghana. The study involved a cross-sectional survey of 371 loan customers of leading financial service providers in Ghana. The results show that satisfaction, trust and loan information quality are significant factors influencing clients’ recommendation of loan products. Moreover, satisfaction and information quality also contributed significantly to influencing clients’ trust for financial service providers. The findings provide important implications for inducing loan clients’ recommendation of financial services to others. While this study is limited in terms of generalizability of the findings in developing countries, it provides avenues for further research for modelling the determinants of loan acquisition intentions of clients in financial markets in other research settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".